Merge pull request #39 from graphcore-research/refactor-encode-round

Refactor encode/round

Author: awf

src/gfloat/__init__.py

@@ -9,8 +9,10 @@
 )
 from .decode import decode_float
 from .printing import float_pow2str, float_tilde_unless_roundtrip_str
-from .round import encode_float, round_float
-from .round_ndarray import encode_ndarray, round_ndarray
+from .round import round_float
+from .encode import encode_float
+from .round_ndarray import round_ndarray
+from .encode_ndarray import encode_ndarray
 from .decode_ndarray import decode_ndarray
 from .types import FloatClass, FloatValue, FormatInfo, RoundMode
 

src/gfloat/block.py

@@ -10,7 +10,8 @@
 import numpy.typing as npt
 
 from .decode import decode_float
-from .round import RoundMode, encode_float, round_float
+from .round import RoundMode, round_float
+from .encode import encode_float
 from .types import FormatInfo
 
 

src/gfloat/encode.py

@@ -0,0 +1,98 @@
+# Copyright (c) 2024 Graphcore Ltd. All rights reserved.
+
+import math
+
+import numpy as np
+
+from .types import FormatInfo
+
+
+def encode_float(fi: FormatInfo, v: float) -> int:
+    """
+    Encode input to the given :py:class:`FormatInfo`.
+
+    Will round toward zero if :paramref:`v` is not in the value set.
+    Will saturate to `Inf`, `NaN`, `fi.max` in order of precedence.
+    Encode -0 to 0 if not `fi.has_nz`
+
+    For other roundings and saturations, call :func:`round_float` first.
+
+    Args:
+      fi (FormatInfo): Describes the target format
+      v (float): The value to be encoded.
+
+    Returns:
+      The integer code point
+    """
+
+    # Format Constants
+    k = fi.bits
+    p = fi.precision
+    t = p - 1
+
+    # Encode
+    if np.isnan(v):
+        return fi.code_of_nan
+
+    # Overflow/underflow
+    if v > fi.max:
+        if fi.has_infs:
+            return fi.code_of_posinf
+        if fi.num_nans > 0:
+            return fi.code_of_nan
+        return fi.code_of_max
+
+    if v < fi.min:
+        if fi.has_infs:
+            return fi.code_of_neginf
+        if fi.num_nans > 0:
+            return fi.code_of_nan
+        return fi.code_of_min
+
+    # Finite values
+    sign = fi.is_signed and np.signbit(v)
+    vpos = -v if sign else v
+
+    if fi.has_subnormals and vpos <= fi.smallest_subnormal / 2:
+        isig = 0
+        biased_exp = 0
+    else:
+        sig, exp = np.frexp(vpos)
+        exp = int(exp)  # All calculations in Python ints
+
+        # sig in range [0.5, 1)
+        sig *= 2
+        exp -= 1
+        # now sig in range [1, 2)
+
+        biased_exp = exp + fi.expBias
+        if biased_exp < 1 and fi.has_subnormals:
+            # subnormal
+            sig *= 2.0 ** (biased_exp - 1)
+            biased_exp = 0
+            assert vpos == sig * 2 ** (1 - fi.expBias)
+        else:
+            if sig > 0:
+                sig -= 1.0
+
+        isig = math.floor(sig * 2**t)
+
+    # Zero
+    if isig == 0 and biased_exp == 0 and fi.has_zero:
+        if sign and fi.has_nz:
+            return fi.code_of_negzero
+        else:
+            return fi.code_of_zero
+
+    # Nonzero
+    assert isig < 2**t
+    assert biased_exp < 2**fi.expBits or fi.is_twos_complement
+
+    # Handle two's complement encoding
+    if fi.is_twos_complement and sign:
+        isig = (1 << t) - isig
+
+    # Pack values into a single integer
+    code = (int(sign) << (k - 1)) | (biased_exp << t) | (isig << 0)
+
+    return code

src/gfloat/encode_ndarray.py

@@ -0,0 +1,84 @@
+# Copyright (c) 2024 Graphcore Ltd. All rights reserved.
+
+from .types import FormatInfo
+import numpy as np
+
+
+def encode_ndarray(fi: FormatInfo, v: np.ndarray) -> np.ndarray:
+    """
+    Vectorized version of :meth:`encode_float`.
+
+    Encode inputs to the given :py:class:`FormatInfo`.
+
+    Will round toward zero if :paramref:`v` is not in the value set.
+    Will saturate to `Inf`, `NaN`, `fi.max` in order of precedence.
+    Encode -0 to 0 if not `fi.has_nz`
+
+    For other roundings and saturations, call :func:`round_ndarray` first.
+
+    Args:
+      fi (FormatInfo): Describes the target format
+      v (float array): The value to be encoded.
+
+    Returns:
+      The integer code point
+    """
+    k = fi.bits
+    p = fi.precision
+    t = p - 1
+
+    sign = np.signbit(v) & fi.is_signed
+    vpos = np.where(sign, -v, v)
+
+    nan_mask = np.isnan(v)
+
+    code = np.zeros_like(v, dtype=np.uint64)
+
+    if fi.num_nans > 0:
+        code[nan_mask] = fi.code_of_nan
+    else:
+        assert not np.any(nan_mask)
+
+    if fi.has_infs:
+        code[v > fi.max] = fi.code_of_posinf
+        code[v < fi.min] = fi.code_of_neginf
+    else:
+        code[v > fi.max] = fi.code_of_nan if fi.num_nans > 0 else fi.code_of_max
+        code[v < fi.min] = fi.code_of_nan if fi.num_nans > 0 else fi.code_of_min
+
+    if fi.has_zero:
+        if fi.has_nz:
+            code[v == 0] = np.where(sign[v == 0], fi.code_of_negzero, fi.code_of_zero)
+        else:
+            code[v == 0] = fi.code_of_zero
+
+    finite_mask = (code == 0) & (v != 0)
+    assert not np.any(np.isnan(vpos[finite_mask]))
+    if np.any(finite_mask):
+        finite_vpos = vpos[finite_mask]
+        finite_sign = sign[finite_mask]
+
+        sig, exp = np.frexp(finite_vpos)
+
+        biased_exp = exp.astype(np.int64) + (fi.expBias - 1)
+        subnormal_mask = (biased_exp < 1) & fi.has_subnormals
+
+        biased_exp_safe = np.where(subnormal_mask, biased_exp, 0)
+        tsig = np.where(subnormal_mask, np.ldexp(sig, biased_exp_safe), sig * 2 - 1.0)
+        biased_exp[subnormal_mask] = 0
+
+        isig = np.floor(np.ldexp(tsig, t)).astype(np.int64)
+
+        zero_mask = fi.has_zero & (isig == 0) & (biased_exp == 0)
+        if not fi.has_nz:
+            finite_sign[zero_mask] = False
+
+        # Handle two's complement encoding
+        if fi.is_twos_complement:
+            isig[finite_sign] = (1 << t) - isig[finite_sign]
+
+        code[finite_mask] = (
+            (finite_sign.astype(int) << (k - 1)) | (biased_exp << t) | (isig << 0)
+        )
+
+    return code

src/gfloat/round.py

@@ -166,94 +166,3 @@ def round_float(
         result = -result
 
     return result
-
-
-def encode_float(fi: FormatInfo, v: float) -> int:
-    """
-    Encode input to the given :py:class:`FormatInfo`.
-
-    Will round toward zero if :paramref:`v` is not in the value set.
-    Will saturate to `Inf`, `NaN`, `fi.max` in order of precedence.
-    Encode -0 to 0 if not `fi.has_nz`
-
-    For other roundings and saturations, call :func:`round_float` first.
-
-    Args:
-      fi (FormatInfo): Describes the target format
-      v (float): The value to be encoded.
-
-    Returns:
-      The integer code point
-    """
-
-    # Format Constants
-    k = fi.bits
-    p = fi.precision
-    t = p - 1
-
-    # Encode
-    if np.isnan(v):
-        return fi.code_of_nan
-
-    # Overflow/underflow
-    if v > fi.max:
-        if fi.has_infs:
-            return fi.code_of_posinf
-        if fi.num_nans > 0:
-            return fi.code_of_nan
-        return fi.code_of_max
-
-    if v < fi.min:
-        if fi.has_infs:
-            return fi.code_of_neginf
-        if fi.num_nans > 0:
-            return fi.code_of_nan
-        return fi.code_of_min
-
-    # Finite values
-    sign = fi.is_signed and np.signbit(v)
-    vpos = -v if sign else v
-
-    if fi.has_subnormals and vpos <= fi.smallest_subnormal / 2:
-        isig = 0
-        biased_exp = 0
-    else:
-        sig, exp = np.frexp(vpos)
-        exp = int(exp)  # All calculations in Python ints
-
-        # sig in range [0.5, 1)
-        sig *= 2
-        exp -= 1
-        # now sig in range [1, 2)
-
-        biased_exp = exp + fi.expBias
-        if biased_exp < 1 and fi.has_subnormals:
-            # subnormal
-            sig *= 2.0 ** (biased_exp - 1)
-            biased_exp = 0
-            assert vpos == sig * 2 ** (1 - fi.expBias)
-        else:
-            if sig > 0:
-                sig -= 1.0
-
-        isig = math.floor(sig * 2**t)
-
-    # Zero
-    if isig == 0 and biased_exp == 0 and fi.has_zero:
-        if sign and fi.has_nz:
-            return fi.code_of_negzero
-        else:
-            return fi.code_of_zero
-
-    # Nonzero
-    assert isig < 2**t
-    assert biased_exp < 2**fi.expBits or fi.is_twos_complement
-
-    # Handle two's complement encoding
-    if fi.is_twos_complement and sign:
-        isig = (1 << t) - isig
-
-    # Pack values into a single integer
-    code = (int(sign) << (k - 1)) | (biased_exp << t) | (isig << 0)
-
-    return code

src/gfloat/round_ndarray.py

@@ -150,83 +150,3 @@ def round_ndarray(
         result = np.where(result == 0, 0.0, result)
 
     return result
-
-
-def encode_ndarray(fi: FormatInfo, v: np.ndarray) -> np.ndarray:
-    """
-    Vectorized version of :meth:`encode_float`.
-
-    Encode inputs to the given :py:class:`FormatInfo`.
-
-    Will round toward zero if :paramref:`v` is not in the value set.
-    Will saturate to `Inf`, `NaN`, `fi.max` in order of precedence.
-    Encode -0 to 0 if not `fi.has_nz`
-
-    For other roundings and saturations, call :func:`round_ndarray` first.
-
-    Args:
-      fi (FormatInfo): Describes the target format
-      v (float array): The value to be encoded.
-
-    Returns:
-      The integer code point
-    """
-    k = fi.bits
-    p = fi.precision
-    t = p - 1
-
-    sign = np.signbit(v) & fi.is_signed
-    vpos = np.where(sign, -v, v)
-
-    nan_mask = np.isnan(v)
-
-    code = np.zeros_like(v, dtype=np.uint64)
-
-    if fi.num_nans > 0:
-        code[nan_mask] = fi.code_of_nan
-    else:
-        assert not np.any(nan_mask)
-
-    if fi.has_infs:
-        code[v > fi.max] = fi.code_of_posinf
-        code[v < fi.min] = fi.code_of_neginf
-    else:
-        code[v > fi.max] = fi.code_of_nan if fi.num_nans > 0 else fi.code_of_max
-        code[v < fi.min] = fi.code_of_nan if fi.num_nans > 0 else fi.code_of_min
-
-    if fi.has_zero:
-        if fi.has_nz:
-            code[v == 0] = np.where(sign[v == 0], fi.code_of_negzero, fi.code_of_zero)
-        else:
-            code[v == 0] = fi.code_of_zero
-
-    finite_mask = (code == 0) & (v != 0)
-    assert not np.any(np.isnan(vpos[finite_mask]))
-    if np.any(finite_mask):
-        finite_vpos = vpos[finite_mask]
-        finite_sign = sign[finite_mask]
-
-        sig, exp = np.frexp(finite_vpos)
-
-        biased_exp = exp.astype(np.int64) + (fi.expBias - 1)
-        subnormal_mask = (biased_exp < 1) & fi.has_subnormals
-
-        biased_exp_safe = np.where(subnormal_mask, biased_exp, 0)
-        tsig = np.where(subnormal_mask, np.ldexp(sig, biased_exp_safe), sig * 2 - 1.0)
-        biased_exp[subnormal_mask] = 0
-
-        isig = np.floor(np.ldexp(tsig, t)).astype(np.int64)
-
-        zero_mask = fi.has_zero & (isig == 0) & (biased_exp == 0)
-        if not fi.has_nz:
-            finite_sign[zero_mask] = False
-
-        # Handle two's complement encoding
-        if fi.is_twos_complement:
-            isig[finite_sign] = (1 << t) - isig[finite_sign]
-
-        code[finite_mask] = (
-            (finite_sign.astype(int) << (k - 1)) | (biased_exp << t) | (isig << 0)
-        )
-
-    return code